ABSTRACT
The world has now looked towards installing more renewable energy sources type distributed generation (DG), such as solar photovoltaic DG (SPVDG), because of its advantages to the environment and the quality of power supply it produces. However, these sources' optimal placement and size are determined before their accommodation in the power distribution system (PDS). This is to avoid an increase in power loss and deviations in the voltage profile. Furthermore, in this article, solar PV is integrated with battery energy storage systems (BESS) to compensate for the shortcomings of SPVDG as well as the reduction in peak demand. This paper presented a novel coronavirus herd immunity optimizer algorithm for the optimal accommodation of SPVDG with BESS in the PDS. The proposed algorithm is centered on the herd immunity approach to combat the COVID-19 virus. The problem formulation is focused on the optimal accommodation of SPVDG and BESS to reduce the power loss and enhance the voltage profile of the PDS. Moreover, voltage limits, maximum current limits, and BESS charge-discharge constraints are validated during the optimization. Moreover, the hourly variation of SPVDG generation and load profile with seasonal impact is examined in this study. IEEE 33 and 69 bus PDSs are tested for the development of the presented work. The suggested algorithm showed its effectiveness and accuracy compared to different optimization techniques. © 2023 TÜBÍTAK.
ABSTRACT
This paper assesses Africa's energy future in a changing global climate to inform development policy. In the midst of economic and energy challenges, coupled with COVID-19 pandemic recovery, Africa is expected to meet global climate obligations. While it makes sense to develop climate resilient economies, Africa appears stuck in its pre-climate change energy delivery systems. The economic, social and environmental consequences of this stagnation are suboptimal for the continent, calling for urgent transition to more sustainable energy delivery. The study analyzed fossil fuel use in Africa from 1960 to 2016, based on a modified Hotelling Rule. Through time series data, the sustainable marginal cost of energy for Africa was estimated. The study further derived the optimal point in time when Africa should switch from fossil fuels as a main source of electricity generation to renewable energy, due to climate change. The study finds more than 70 percent increase in the marginal cost of fossil fuel compared to a cumulative reduction of 80 percent in the marginal cost of solar photovoltaic over the study period. Also, the switch point to renewable energy as the main source of electricity in Africa was found to be 2003. For sustainable delivery of energy in Africa, the study recommends policies to internalize the externalities of fossil fuel, backed by recovery subsidies to make up for the loss of welfare from fossil fuel use, and to create an enabling environment for a speedy energy transition in Africa's changing climate. © 2020 AESS Publications. All Rights Reserved.
ABSTRACT
Despite the COVID-19 pandemic, the global photovoltaic (PV) market grew significantly again in 2021, further enhancing the vital role of solar power in the battle against global climate change. One of the main reasons for the rapid growth of this market is that PV panels are almost maintenance-free after deployment, thereby low Levelized cost of solar power. However, this does not mean that PV panels will not fail in service. In fact, they may suffer from performance degradation, structural failure, or even complete loss of power generation capacity during operation. If these problems cannot be detected and solved in time, they may also bring significant economic losses to the operators. However, a large-scale solar power plant will contain hundreds of thousands of PV panels. How to quickly identify those defective ones from so many PV panels is a quite challenging issue. The research of this paper is to address this issue with the aid of intelligent image processing technology. In this study, an intelligent PV panel condition monitoring technique is developed using machine learning algorithms. It can rapidly process, analyze and classify the thermal images of PV panels collected from solar power plants. Therefore, it not only can quickly identify those defective PV panels but also can accurately diagnose the defect types of the PV panels. It is deemed that the successful development of such a technology will be of great significance to further strengthen the scientific management of solar power assets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Building Applied Photovoltaics (BAPV) such as Roof-top Solar PV has gained significant attention in recent years for harnessing the untapped potential of renewable energy sources. However, rooftop PV poses hurdles of space restriction and shadowing in densely packed urban residential neighborhoods. This study aims to design and assess the feasibility of an integrated grid-connected Rooftop and Façade Building Integrated Photovoltaic (BIPV) for meeting the energy demand of residential buildings on an academic campus. Three distinctive groups of residential typologies have been investigated in this study, categorized based on built area and occupants' past energy usage. Additionally, the variation in the measured Energy Performance index of the three different residential groups is illustrated to pave the path for the development of a typology-based residential energy benchmarking and labelling system. The Solar PV system has been designed for the maximum household energy demand recorded in CoVID-affected years due to high residential electricity usage in this period. The study showcases that integration of façade BIPV for low-rise residential buildings increases the system energy production to up to 62.5 % based on the utilized surface area for active PV. Furthermore, the Net Zero Energy Building (ZEB) potential for each typology has been achieved by integration of the proposed Solar PV, evaluated as a function of the Energy Performance Index (EPI) and Energy Generation Index (EGI). The designed nominal PV power of the proposed grid-connected plant is 5.6 MW, producing 7182 MWh annually, meeting the maximum residential energy demand in the studied academic campus in CoVID affected year. © 2022 Elsevier B.V.
ABSTRACT
There are seven types of renewable energy, of which only five will generate electricity. The most extensively utilized renewable energy source for residential usage is solar photovoltaic energy. With over 486GW of generation capacity, solar PV remains as the third largest renewable power source, with advanced photovoltaic (PV) innovation. The United States and China have the most solar plant installations. India holds the third place as the country's solar area is expected to recover completely after the COVID-19 epidemic, wherein the energy from solar PV is expected to overtake coal before 2040. To examine and observe the various processes carried out by solar PV, several experimental studies have been carried out. This research article presents the different case studies of solar PV systems and observes the characteristics of voltage and current or voltage and power for different solar radiations and temperature, respectively. Secondly, the proposed study has observed and analyzed the performance of PV module in series or parallel connections concerning I-V and V-P characteristics. In third section, the shading effects on solar PV module output power is observed. In the fourth and fifth sections, methods to solve the shading module's output power constraint has been studied and finally the MPP is observed by varying the duty cycle of the converter. In the sixth and seventh section, the performance of SEPIC converter is evaluated based on the open and closed-loop systems, and the challenges in buck-boost converter are solved. © 2022 IEEE.
ABSTRACT
This paper provides an effective method for optimal sizing and allocation of DGs & D-STATCOM placement to minimize the actual power losses and improve voltage profile in RDS (Radial Distribution System) with incorporating effect of load growth & load modelling. The technique's legitimacy is tried on the standard IEEE 33-bus RDS by performing load flow analysis after compensating the candidate bus. The outcomes acquired are contrasted with and without the Solar Photovoltaic Panel based DG (PVDG), Wind Turbine based DG (WTDG) and D-STATCOM for minimum actual power loss. Further the changes in the operational circumstances of PVDG and WTDG as well as D-STATCOM, are also investigated in order to fulfil the shifting load profile while preserving the voltage constraint and minimizing real power loss owing to the COVID-19 pandemic. The variation in operational setting and the power supplied to the grid for compensating the coal-based generation during the lockdown, pan-India lights off event and Unlock 1 are also studied in the paper. © 2022 IEEE.
ABSTRACT
This paper presents HOMER simulation models for optimizing renewable energy system components in Off grid and Grid connected systems for Covid-19 hospitals. Load profiles of 100 bed and 50 bed Covid hospitals are established critically. Two simulation models are built for each load profile. Optimization of energy system components is conducted based on net present cost and cost of energy. In process of optimization, HOMER simulates all possible configurations and ranks feasible combinations according to TNPC, and lists feasible options. Ten combinations are identified for analysis in each model. The critical analysis of results revealed that, grid connected system proves to be most economic and reliable option for hospital loads. The cost of energy obtained in grid connected system is Rs.9/kWh, whereas for diesel generated connected system costing Rs.22/kWh. This difference is appeared mainly due to higher cost of diesel and variation in the solar and wind energy potential with time of the day. Thus it is concluded that, renewable energy sources can be effectively employed for energizing the Covid-19 hospital in association with grid. © 2021 IEEE.
ABSTRACT
COVID-19 em ergency has ca used major changes in everyday life in the la st m onths, a nd it a lso affected the management of buildings. In particular, indoor a ir quality and ventilation ha ve been considered to play a key role in the spreading of the infection, causing national and interna tional subjects to draw up specific guidelines on ventilation and air recirculation rate in AHUs. The pa per deals with the “Loccioni Leaf Lab”, a n industria l building that hosts offices a nd workers operating on test benches. The building features high performance envelope, solar photovoltaic systems, groundwater heat pumps a nd a high -technology control a nd monitoring system and it is connected to a thermal and electric smart grid. A va lidated m odel of the building, im plem ented with the software DesignBuilder a nd EnergyPlus, wa s used to carry out numerical sim ula tions to optimize the m anagement of the HVAC through the Building Management System. Different working conditions have been sim ulated, a nd the numerical output has been used together with experimental data collected from the Company monitoring system. Ithas been possible to investigate how the extra ventilation required by the new guidelines would affect the tota l energy consumption a nd to compare, in term s of energy efficiency, the different HVAC m a nagement stra tegies tha t could be used to ensure occupants hea lth safety and indoor air qua lity. © 2021 Institute of Physics Publishing. All rights reserved.